MLLPA

MLLPA predicts lipid phases and maps local lipid environments from molecular dynamics (MD) simulation data using machine learning.

Key Features:

• Phase Composition Analysis: Labels each lipid molecule according to its state and provides detailed composition analysis of membrane phases.
• Machine Learning Classification: Implements machine learning algorithms in Python 3 to classify lipid states from MD data.
• Voronoi Tessellation: Employs Voronoi tessellations to decompose the simulation box and examine local environments around lipids.
• Input Compatibility: Reads standard coordinate and trajectory files and supports formats from GROMACS and LAMMPS, compatible with all-atom CHARMM36 and coarse-grain Martini models.
• Multi-geometry Analysis: Analyzes multiple membrane geometries, including bilayers and vesicles.
• Multiple Phase Coexistence: Supports training and analysis with more than two coexisting phases.

Scientific Applications:

• Lipid membrane organization and phase characterization: Provides per-lipid phase labels and composition metrics to study membrane organization.
• Membrane dynamics and stability studies: Enables analysis of membrane dynamics, stability, and molecular-level interactions.
• Cellular processes and drug delivery research: Applicable to investigations of cellular membrane processes and drug delivery systems.
• Biomimetic materials development: Supports analyses relevant to the design and evaluation of biomimetic materials.

Methodology:

Reads standard simulation coordinate and trajectory files to extract lipid molecular states, applies machine learning classification to assign lipid phases, and performs spatial decomposition using Voronoi tessellations to assess local environments.